Optimizing a partition in data deduplication

ABSTRACT

For optimizing a partition of a data block into matching and non-matching segments in data deduplication using a processor device in a computing environment, an optimal calculation operation is applied in polynomial time to the matching segments for selecting a globally optimal subset of a set of matching segments according to overhead considerations for minimizing an overall size of a deduplicated file by determining a trade off between a time complexity and a space complexity.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates in general to computers, and moreparticularly for optimizing a partition in data deduplication in acomputing environment.

2. Description of the Related Art

In today's society, computer systems are commonplace. Computer systemsmay be found in the workplace, at home, or at school. Computer systemsmay include data storage systems, or disk storage systems, to processand store data. Large amounts of data have to be processed daily and thecurrent trend suggests that these amounts will continue beingever-increasing in the foreseeable future. An efficient way to alleviatethe problem is by using deduplication. The idea underlying adeduplication system is to exploit the fact that large parts of theavailable data is copied again and again and forwarded without anychange, by locating repeated data and storing only its first occurrence.Subsequent copies are replaced with pointers to the stored occurrence,which significantly reduces the storage requirements if the data isindeed repetitive.

SUMMARY OF THE DESCRIBED EMBODIMENTS

In one embodiment, a method is provided for optimizing a partition of adata block into matching and non-matching segments in data deduplicationusing a processor device, in a computing environment. In one embodiment,by way of example only, an optimal calculation operation is applied inpolynomial time to the matching segments for selecting a globallyoptimal subset of a set of matching segments according to overheadconsiderations for minimizing an overall size of a deduplicated file bydetermining a trade off between a time complexity and a spacecomplexity.

In another embodiment, a computer system is provided for optimizing apartition of a data block into matching and non-matching segments indata deduplication using a processor device, in a computing environment.The computer system includes a computer-readable medium and a processorin operable communication with the computer-readable medium. In oneembodiment, by way of example only, the processor, applies an optimalcalculation operation in polynomial time to the matching segments forselecting a globally optimal subset of a set of matching segmentsaccording to overhead considerations for minimizing an overall size of adeduplicated file by determining a trade off between a time complexityand a space complexity.

In a further embodiment, a computer program product is provided foroptimizing a partition of a data block into matching and non-matchingsegments in data deduplication using a processor device, in a computingenvironment. The computer-readable storage medium has computer-readableprogram code portions stored thereon. The computer-readable program codeportions include a first executable portion that, applies an optimalcalculation operation in polynomial time to the matching segments forselecting a globally optimal subset of a set of matching segmentsaccording to overhead considerations for minimizing an overall size of adeduplicated file by determining a trade off between a time complexityand a space complexity.

In addition to the foregoing exemplary method embodiment, otherexemplary system and computer product embodiments are provided andsupply related advantages. The foregoing summary has been provided tointroduce a selection of concepts in a simplified form that are furtherdescribed below in the Detailed Description. This Summary is notintended to identify key features or essential features of the claimedsubject matter, nor is it intended to be used as an aid in determiningthe scope of the claimed subject matter. The claimed subject matter isnot limited to implementations that solve any or all disadvantages notedin the background.

BRIEF DESCRIPTION OF THE DRAWINGS

In order that the advantages of the invention will be readilyunderstood, a more particular description of the invention brieflydescribed above will be rendered by reference to specific embodimentsthat are illustrated in the appended drawings. Understanding that thesedrawings depict embodiments of the invention and are not therefore to beconsidered to be limiting of its scope, the invention will be describedand explained with additional specificity and detail through the use ofthe accompanying drawings, in which:

FIG. 1 is a block diagram illustrating a computing system environmenthaving an example storage device in which aspects of the presentinvention may be realized;

FIG. 2 is a block diagram illustrating a hardware structure of datastorage system in a computer system in which aspects of the presentinvention may be realized;

FIG. 3 is a diagram illustrating a partition of a data chunk in acomputer system in which aspects of the present invention may berealized;

FIG. 4 is a flowchart illustrating an exemplary method for optimizing apartition in data deduplication in which aspects of the presentinvention may be realized;

FIG. 5 is a diagram illustrating an optimal partition built by means oftwo-dimensional dynamic programming tables in which aspects of thepresent invention may be realized; and

FIG. 6 is block diagram illustrating an alternative partition of asub-range in which aspects of the present invention may be realized.

DETAILED DESCRIPTION OF THE DRAWINGS

Data deduplication refers to the reduction and/or elimination ofredundant data. In data deduplication, a data object, which may be afile, a data stream, or some other form of data, is broken down into oneor more parts called sub-blocks. In a data deduplication process,duplicate copies of data are reduced or eliminated, leaving a minimalamount of redundant copies, or a single copy of the data, respectively.A data deduplication system uses some mechanism to identify substringsof data that have already been observed and are currently stored in itsstorage subsystem (e.g. referred to as “matches”). These substrings ofpreviously existing data may be contiguous or may be separated bysubstrings of data that have not been previously observed (e.g. referredto as “mismatches” or “non-matches”). The output of this identifyingprocess (which shall be referred to as the input of the presentinvention described herein) is a list of matches, each consisting of apair of pointers, one to the source, one to the destination, and thesize of the matching substring. More precisely, a list of matchesinduces a partition (e.g., an input partition) of the input data intosubstrings that are either matches or mismatches, and this partition isthe input to the process described herein.

Each match incurs a metadata overhead (e.g. a pointer to where thecommon data can be found). The small matches must be weighed against thecost of maintaining this metadata. In addition, as data is modified, itis natural for matches to show a level of fragmentation. This addsoverhead when deduplicated data must be reconstituted, for example forrestore purposes. The deduplication system must weigh the pros and consof each match in isolation and in conjunction with all the othermatches. This weighing the pros and cons of each match in isolation andin conjunction with all the other matches is the focus of the presentinvention as described herein.

A simplistic solution may be to build the output by just copying theinput. In other words, accept exactly the partition found by listing allthe matches. However, such a solution ignores the fact that at least apart of the matches are not worth being retained, as they might causetoo high of a degree of fragmentation, or require too much metadataoverhead. The challenge is therefore to decide which matches should bekept, and which should be ignored. Therefore, the illustratedembodiments provide a solution for optimizing a partition in datadeduplication. In one embodiment, by way of example only, for modifyingan input partition of a data block having both matching segments andnon-matching segments, an optimal calculation operation is applied inpolynomial time to the matching segments for selecting a globallyoptimal subset of the input partition according to overheadconsiderations for minimizing a deduplicated file (e.g., an overall sizeof the deduplicated file) by determining a trade off for both a timecomplexity and a space complexity.

Turning now to FIG. 1, exemplary architecture 10 of a computing systemenvironment is depicted. The computer system 10 includes centralprocessing unit (CPU) 12, which is connected to communication port 18and memory device 16. The communication port 18 is in communication witha communication network 20. The communication network 20 and storagenetwork may be configured to be in communication with server (hosts) 24and storage systems, which may include storage devices 14. The storagesystems may include hard disk drive (HDD) devices, solid-state devices(SSD) etc., which may be configured in a redundant array of independentdisks (RAID). The operations as described below may be executed onstorage device(s) 14, located in system 10 or elsewhere and may havemultiple memory devices 16 working independently and/or in conjunctionwith other CPU devices 12. Memory device 16 may include such memory aselectrically erasable programmable read only memory (EEPROM) or a hostof related devices. Memory device 16 and storage devices 14 areconnected to CPU 12 via a signal-bearing medium. In addition, CPU 12 isconnected through communication port 18 to a communication network 20,having an attached plurality of additional computer host systems 24. Inaddition, memory device 16 and the CPU 12 may be embedded and includedin each component of the computing system 10. Each storage system mayalso include separate and/or distinct memory devices 16 and CPU 12 thatwork in conjunction or as a separate memory device 16 and/or CPU 12.

FIG. 2 is an exemplary block diagram 200 showing a hardware structure ofa data storage system in a computer system according to the presentinvention. Host computers 210, 220, 225, are shown, each acting as acentral processing unit for performing data processing as part of a datastorage system 200. The cluster hosts/nodes (physical or virtualdevices), 210, 220, and 225 may be one or more new physical devices orlogical devices to accomplish the purposes of the present invention inthe data storage system 200. In one embodiment, by way of example only,a data storage system 200 may be implemented as IBM® ProtecTIER®deduplication system TS7650G™. A Network connection 260 may be a fibrechannel fabric, a fibre channel point to point link, a fibre channelover ethernet fabric or point to point link, a FICON or ESCON I/Ointerface, any other I/O interface type, a wireless network, a wirednetwork, a LAN, a WAN, heterogeneous, homogeneous, public (i.e. theInternet), private, or any combination thereof. The hosts, 210, 220, and225 may be local or distributed among one or more locations and may beequipped with any type of fabric (or fabric channel) (not shown in FIG.2) or network adapter 260 to the storage controller 240, such as Fibrechannel, FICON, ESCON, Ethernet, fiber optic, wireless, or coaxialadapters. Data storage system 200 is accordingly equipped with asuitable fabric (not shown in FIG. 2) or network adaptor 260 tocommunicate. Data storage system 200 is depicted in FIG. 2 comprisingstorage controllers 240 and cluster hosts 210, 220, and 225. The clusterhosts 210, 220, and 225 may include cluster nodes.

To facilitate a clearer understanding of the methods described herein,storage controller 240 is shown in FIG. 2 as a single processing unit,including a microprocessor 242, system memory 243 and nonvolatilestorage (“NVS”) 216. It is noted that in some embodiments, storagecontroller 240 is comprised of multiple processing units, each withtheir own processor complex and system memory, and interconnected by adedicated network within data storage system 200. Storage 230 (labeledas 230 a, 230 b, and 230 n in FIG. 3) may be comprised of one or morestorage devices, such as storage arrays, which are connected to storagecontroller 240 (by a storage network) with one or more cluster hosts210, 220, and 225 connected to each storage controller 240.

In some embodiments, the devices included in storage 230 may beconnected in a loop architecture. Storage controller 240 manages storage230 and facilitates the processing of write and read requests intendedfor storage 230. The system memory 243 of storage controller 240 storesprogram instructions and data, which the processor 242 may access forexecuting functions and method steps of the present invention forexecuting and managing storage 230 as described herein. In oneembodiment, system memory 243 includes, is in association with, or is incommunication with the operation software 250 for performing methods andoperations described herein. As shown in FIG. 2, system memory 243 mayalso include or be in communication with a cache 245 for storage 230,also referred to herein as a “cache memory”, for buffering “write data”and “read data”, which respectively refer to write/read requests andtheir associated data. In one embodiment, cache 245 is allocated in adevice external to system memory 243, yet remains accessible bymicroprocessor 242 and may serve to provide additional security againstdata loss, in addition to carrying out the operations as described inherein.

In some embodiments, cache 245 is implemented with a volatile memory andnon-volatile memory and coupled to microprocessor 242 via a local bus(not shown in FIG. 2) for enhanced performance of data storage system200. The NVS 216 included in data storage controller is accessible bymicroprocessor 242 and serves to provide additional support foroperations and execution of the present invention as described in otherfigures. The NVS 216, may also referred to as a “persistent” cache, or“cache memory” and is implemented with nonvolatile memory that may ormay not utilize external power to retain data stored therein. The NVSmay be stored in and with the cache 245 for any purposes suited toaccomplish the objectives of the present invention. In some embodiments,a backup power source (not shown in FIG. 2), such as a battery, suppliesNVS 216 with sufficient power to retain the data stored therein in caseof power loss to data storage system 200. In certain embodiments, thecapacity of NVS 216 is less than or equal to the total capacity of cache245.

Storage 230 may be physically comprised of one or more storage devices,such as storage arrays. A storage array is a logical grouping ofindividual storage devices, such as a hard disk. In certain embodiments,storage 230 is comprised of a JBOD (Just a Bunch of Disks) array or aRAID (Redundant Array of Independent Disks) array. A collection ofphysical storage arrays may be further combined to form a rank, whichdissociates the physical storage from the logical configuration. Thestorage space in a rank may be allocated into logical volumes, whichdefine the storage location specified in a write/read request.

In one embodiment, by way of example only, the storage system as shownin FIG. 2 may include a logical volume, or simply “volume,” may havedifferent kinds of allocations. Storage 230 a, 230 b and 230 n are shownas ranks in data storage system 200, and are referred to herein as rank230 a, 230 b and 230 n. Ranks may be local to data storage system 200,or may be located at a physically remote location. In other words, alocal storage controller may connect with a remote storage controllerand manage storage at the remote location. Rank 230 a is shownconfigured with two entire volumes, 234 and 236, as well as one partialvolume 232 a. Rank 230 b is shown with another partial volume 232 b.Thus volume 232 is allocated across ranks 230 a and 230 b. Rank 230 n isshown as being fully allocated to volume 238—that is, rank 230 n refersto the entire physical storage for volume 238. From the above examples,it will be appreciated that a rank may be configured to include one ormore partial and/or entire volumes. Volumes and ranks may further bedivided into so-called “tracks,” which represent a fixed block ofstorage. A track is therefore associated with a given volume and may begiven a given rank.

The storage controller 240 may include a partition optimizing module255. The partition optimizing module 255 may work in conjunction witheach and every component of the storage controller 240, the hosts 210,220, 225, and storage devices 230. The partition optimizing module 255may be structurally one complete module or may be associated and/orincluded with other individual modules. The partition optimizing module255 may also be located in the cache 245 or other components.

The storage controller 240 includes a control switch 241 for controllingthe fiber channel protocol to the host computers 210, 220, 225, amicroprocessor 242 for controlling all the storage controller 240, anonvolatile control memory 243 for storing a microprogram (operationsoftware) 250 for controlling the operation of storage controller 240,data for control, cache 245 for temporarily storing (buffering) data,and buffers 244 for assisting the cache 245 to read and write data, acontrol switch 241 for controlling a protocol to control data transferto or from the storage devices 230, the partition optimizing module 255,in which information may be set. Multiple buffers 244 may be implementedwith the present invention to assist with the operations as describedherein. In one embodiment, the cluster hosts/nodes, 210, 220, 225 andthe storage controller 240 are connected through a network adaptor (thiscould be a fibre channel) 260 as an interface i.e., via at least oneswitch called “fabric.”

In one embodiment, the host computers or one or more physical or virtualdevices, 210, 220, 225 and the storage controller 240 are connectedthrough a network (this could be a fibre channel) 260 as an interfacei.e., via at least one switch called “fabric.” In one embodiment, theoperation of the system shown in FIG. 2 will be described. Themicroprocessor 242 may control the memory 243 to store commandinformation from the host device (physical or virtual) 210 andinformation for identifying the host device (physical or virtual) 210.The control switch 241, the buffers 244, the cache 245, the operatingsoftware 250, the microprocessor 242, memory 243, NVS 216, partitionoptimizing module 255 are in communication with each other and may beseparate or one individual component(s). Also, several, if not all ofthe components, such as the operation software 250 may be included withthe memory 243. Each of the components within the devices shown may belinked together and may be in communication with each other for purposessuited to the present invention.

As mentioned above, the partition optimizing module 255 may also belocated in the cache 245 or other components. As such, the partitionoptimizing module 255 maybe used as needed, based upon the storagearchitecture and users preferences.

FIG. 3 is a diagram illustrating a partition of a data chunk in acomputer system in which aspects of the present invention may berealized. As illustrated in FIG. 3, non-matches, represented by the greyrectangles, contain new data and are indexed N₁, N₂, . . . , N_(k). TheMatches, drawn as the white/clear rectangles, contain data that haspreviously appeared in the repository, and will be stored by means ofpointers of the form (address, length); the matching parts between thenon-matching blocks N_(i) and N_(i+1) are indexed M_((i,1)), M_((i,2)),( . . . ), M_((i,j) _(i) ₎. The non-matching parts cannot be consecutivebecause this is new data, and any stretch of such new characters isconsidered a new, single part. The matching parts, on the other hand,may consist of several different sub-parts that are located in differentplaces on the disk; each sub-part needs therefore a pointer of its own.To illustrate these definitions, by way of example only, suppose thatthe given text consists of the 22 characters, indexed from 1 to 22:

a b c d e f b c d d e f b c x y z c d e f wThe compressed form would then bea b c d e f (2,3) (4,5) x y z (3,4) wwhere the numbers in parentheses are pointers of the form (address,length). In this specific example, the non-matches are N₁=a b C d e f,N₂=x y z and N₃=w; the matches are M_(1,1)=(2,3), referring to thesubstring starting at address 2 and having length 3, that is, the stringb c d, M_(1,2)=(4,5), referring to the substring starting at address 4and having length 5, that is, the string d e f b C, and M_(2,1)=(3,4),referring to the substring starting at address 3 and having length 4,that is, the string c d e f. FIG. 3 is a schematic view of an extensionof this example, including also elements M_(3,1), M_(3,2), M_(3,3), N₄,M_(4,1), but showing only the elements, and not the characters or(address,length) pairs they represent.

By way of example only, two functions that are considered are definedfor these matching and non-matching parts. A cost function c( ) is usedfor giving the price that is incurred for storing the pointers in themeta-data; typically, all pointers are of fixed length E (e.g, asdescribed herein E is equal to 24 bytes). In other words,c(N_(i))=c(M_(i,j))=24 for all indexes, so that the cost for themeta-data depends only on the number of parts, which is k+Σ_(t=1)^(k)j_(t). The second function s( ) measures, for each part, the size ofthe data on the disk. So that s(N_(i)) will be just the number of bytesof the non-matching part, as these new bytes have to be storedphysically somewhere, and s(M_(i,j))=0, since no new data is written tothe disk for a matching part. However, it is shall be defined thats(M_(i,j))=length for a block M_(i,j) that is stored by means of apointer of the form (address, length), which means that the size will bedefined as the number of bytes written to the disk in case a decision ismade to ignore the fact that M_(i,j) has occurred earlier and thus has amatching part already in the repository.

The compressed data consists of the items written to the disk and alsothe pointers in the meta-data. Yet these cannot necessarily be tradedone to one, as storage space for the meta-data will generally be moreexpensive. As used throughout, an assumption is made that there exists amultiplicative factor F such that, in the calculations described herein,one byte of meta-data may be counted as equivalent to F bytes of datawritten to the disk. This factor need not be constant and maydynamically depend on several run-time parameters. Practically, F willbe stored in a variable and may be updated when necessary, but F shallbe used in the sequel as if it were a constant. Given the abovenotations, the following equation illustrates the size of the compressedfile:

$\begin{matrix}{{{F \cdot E \cdot \left( {k + {\sum\limits_{t = 1}^{k}j_{t}}} \right)} + {\sum\limits_{i = 1}^{k}{s\left( N_{i} \right)}}},} & (1)\end{matrix}$

However, the equation for the size of an uncompressed file isillustrated by:

$\begin{matrix}{\sum\limits_{i = 1}^{k}{\left( {{s\left( N_{i} \right)} + {\sum\limits_{t = 1}^{j_{i}}{s\left( M_{i,t} \right)}}} \right).}} & (2)\end{matrix}$

The optimization problem considered herein is based on the fact that apartition obtained as input may be altered. The non-matching parts N_(i)may not be touched, so the only degree of freedom is to decide, for eachof the matching parts M_(i,j), whether the corresponding pointer shouldbe kept, or whether an alternative choice is selected to ignore thematch and treat the match as if it were a non-matching segment. There isa priori nothing to be gained from such a decision: the pointer in themeta-data is changed from matching to non-matching, but incurs the samecost, and some data has been added to the disk, so there will always bea loss. Notwithstanding, the following example illustrates that a gainmay be achieved in certain cases. Consider the block M_(1,2) in FIG. 3.If a decision is made to ignore its matching counterpart, the data ofM_(1,2) has to be written to the disk, but it is contiguous with thedata of N₂. Thus, the two parts may therefore be fused, which reducesthe number of meta-data entries by one. This will result in a gain ifs(M_(1,2))<F*E. Moreover, if indeed the decision is made to considerM_(1,2) as a non-matching block, this will leave M_(1,1) as a singlematch between two non-matches. In this case, ignoring the match mayallow to unify the three blocks M_(1,1), N₂, reducing the number ofmeta-data entries by two. This will be advantageous even ifs(M_(1,2))<2F*E. More generally, any matching blocks having non-matchingblocks touching on at least one of their sides may be candidates forsuch a fusion, which can trigger even further unifications. Yet theseare not the only cases: even matching blocks touching only othermatching blocks may profit from unification. However, this is not truefor single matching blocks whose neighbors both are also matching, asseen in position M_(3,2) in FIG. 3, because data is added to the disk,but no meta-data is removed, just one of the entries is changed. Butthere might be a stretch of several matching blocks that can profit fromunification.

It should be noted that devising a new partition is not only a matter oftrading a byte of meta-data versus F bytes of disk data. Reducing thenumber of entries in the meta-data has also an effect on the timecomplexity, since each entry requires an additional read operation. Fordealing with such time/space tradeoffs, it is assumed herein that themultiplicative factor F already takes also the time complexity intoaccount. In other words, the multiplicative factor F reflects anestimation price/cost of how many bytes of disk space it will cost inorder to save one byte of meta-data, considering all aspects, includingdisk space, CPU and/or input/output (I/O).

In one embodiment, by way of example only the present invention, asdescribed herein, provides an optimal way to select an appropriatesubset of the input partition that minimizes the size of the compressedfile. In alternative embodiments, the present invention may simplyaccept the original partition or use some simple heuristic. Although,accepting the original partition has the advantage of not requiring anycalculations, merely accepting the original partition may results inmuch waste of space and time resources. Also, using a heuristic such as,for example, keeping the ratio of the number of non-matching elements tothe number of accepted matching elements between predefined limits, mayresult in a faster implementation, but there is no guarantee ofoptimality, so the performance may be very inefficient from the storagepoint of view.

Thus, as illustrated below in FIG. 4, the illustrated embodimentsprovide a solution for optimizing a partition in data deduplication. Inone embodiment, by way of example only, for modifying an input partitionof a data block having both matching segments and non-matching segments,an optimal calculation operation is applied in polynomial time to thematching segments for selecting a globally optimal subset of the inputpartition according to overhead considerations for minimizing adeduplicated file by determining a trade off for both a time complexityand a space complexity. In one embodiment, an optimal calculationoperation is applied individually on each sequence of consecutivematching items (e.g., with M representing the matching items),surrounded on both ends by non-matching items (e.g., with NMrepresenting the non-matching items), since the non-matching cannot bealtered, and the only possible transformation is to declare the matchingblocks as if they were non-matching. Therefore the originally givenNM-items will appear also in the final optimal solution, which allowsfor concentrating and focusing only on each sub-part individually.

For example, consider then the (matching) elements as indexed 1,2, . . ., n and the non-matching delimiters as indexed 0 and n+1. It should benoted that for notation: the required partition is returned in the formof a bit-string of length n, with the bit in position i being set to 1if the i-th ith element should be of type NM, and set to 0 if the i-thelement should be of type M. This notation implies immediately that thenumber of possible solutions is 2″, so that an exhaustive search of thisexponential number of alternatives is ruled out. For example, even on amoderately large input of size n=100, the algorithm would requirebillions of hours of CPU on the strongest computers known today. Thebasis for the non-exponential solution suggested in this invention isthe fact that the optimal partition can be split into sub-parts, each ofwhich must be optimal for the corresponding sub-ranges. Thus, thesolution may be obtained for a given range by trying all the possiblesplits into two sub-parts. Such recursive definitions call for resolvingthe problem by means of dynamic programming. One challenge is that theoptimal solution for the range (i,j) may depend on whether its borderingelements, indexed as i−1 and j+1, are of type matching or non-matching,so the optimal solution for range (i,j) might depend on the optimalsolution on the neighboring ranges. As such, FIG. 4 illustrates thesolution of the present invention for optimizing a partition in a datadeduplication system.

Turning now to FIG. 4, a flowchart illustrating an exemplary method 400for optimizing a partition in data deduplication using a processordevice in a computing environment is illustrated. The method 400 begins(step 402). The method 400 calculates trivial partial solutions forsubranges of size 1 (step 404). The method 400 then assigns the rangesize (range_size) a value of 2 (step 406). Next, the method determinesif the range size (range_size) is equal to n (step 408). If yes, themethod 400 returns the solution for full range (1,n) (step 414). Themethod 400 then ends (step 416). If no, the method applies an optimalcalculation operation/solution in polynomial time to the matchingsegments for selecting a globally optimal subset of the input partitionfor the sub-range of size range_size (step 410). The method incrementsthe range size (range_size) by 1 (step 412).

More specifically, as described below in FIG. 5, for optimizing apartition of a data block into matching and non-matching segments indata deduplication using a processor device in a computing environmentas discussed in FIG. 4, trivial partial solutions for sub-ranges of size1 may be calculated. Then, incrementally larger partial solutions forlarger sub-ranges are calculated based on the already calculated resultsand this process is repeated until the current incrementally largerpartial solution is the full solution, that is, covers the full rangefrom 1 to n.

As described in FIG. 5, below, the globally optimal subset (e.g., theoptimal partition) may be built by means of two-dimensional dynamicprogramming tables. FIG. 5 is a diagram illustrating an optimalpartition built by means of two-dimensional dynamic programming tablesin which aspects of the present invention may be realized. The cost ofthe globally optimal subset (e.g., the optimal partition) may beevaluated by means of a two-dimensional dynamic programming tablerepresented by a matrix C[i,j], and the optimal partition will be storedin a similar table represented by a matrix PS[i,j], that holds theoptimal partition for the given parameters, which is a bit-string oflength j−i+1. In other words, the cost of the globally optimal subset(e.g., the optimal partition) for elements indexed i, i+1, . . . , j−1,j is stored at C[i,j] and the corresponding optimal partition for theseelements is stored at PS[i,j].

For 1≦i≦j≦n, the element C[i,j], is defined as the global cost of theoptimal partition of the sub-sequence of elements i, i+1, . . . , j−1,j, when the surrounding elements i−1 and j+1 are of type NM (e.g., thenon-matching segment types). This cost will be given in bytes andreflect the size of the data on disk for non-matching (NM) elements,plus the size of the meta-data for all the elements, using theequivalence factor explained above. In other words, each meta-data entryincurs a cost of FE bytes (F is the multiplicative factor and isvariable, and E is the length of each pointer in bytes). As illustratedin FIG. 5, once the two-dimensional dynamic programming tables arefilled up, the cost of the optimal solution that is sought for is storedin C[1,n], which is the upper right corner of the matrix highlighted inFIG. 5, and the corresponding partition is given in PS[1,n].

The basis of the calculation, mentioned above in FIGS. 4-5, will be theindividual items themselves stored in the main diagonal of the matrix,C[i,i] of the matrix for 1≦i≦n, as well as the elements just below thediagonal, C[i,i−1]. The elements belonging to a given diagonal appearwith identical shading in FIG. 5. The following iterations will then beordered by increasing difference between i and j. In one embodiment, theiterations will first handle and deal with all sequences of two adjacentelements, which correspond to the diagonal just above the main diagonalin FIG. 5, and then three adjacent elements, corresponding to the nexthigher diagonal in FIG. 5, and continue up to n adjacent elements. Thislast element corresponds to the last diagonal in FIG. 5, consisting onlyof the single box in the upper right corner of the matrix. Whencalculating the optimal solution for a sequence of £ adjacent elements,we can use our knowledge of the optimal solutions for all shortersub-sequences. In fact, for a sequence of length l=j−i+1, the presentinvention only needs to check the sum of the costs (e.g., global cost)of all possible partitions of the range (i,j) into two disjointsub-ranges, that is, the cost for the subrange (i,k−1) plus that of thesubrange (k+1,j) for some index k, such that i<k<j. The costs areinitialized for each subrange by the possibility of leaving all the nelements as the matching type. In other words, the problem the presentinvention wants to solve is to find an optimal partition of the range 1,. . . , n. In one embodiment, one possibility is that the partitionshould not be partitioned at all and leave all elements as matching,symbolized, for example, by the string 0000 . . . 0. In an alternativeembodiment, there are some elements that should be turned into NMelements (which will be symbolized by 1's within the string of 0's).Thus, as described above, it is sufficient to attempt to partition therange into 2 (as opposed to more) subranges. As such, either the optimalpartition is that there should be no partition (that's 00000 . . . 0),or, if that is not true, then there must be at least one element thatwill be changed from type M to type NM. For example, suppose the indexof a particular element that will be changed from type M to type NM isk. Then all the present invention may need to do is recursively find theoptimal partition for the subrange (i, k−1), then for the subrange(k+1,j), and add the costs of the subranges. Doing this for all thepossible values of k provides for the ability to choose the optimumpartition by choosing the minimal sum.

More specifically, as indicated in the pseudo code, illustrated below,in lines 1 and 3, the table is initialized for ranges of size 0, thatis, ranges of type (i+1, i), giving them a cost 0. The line numbersrefer to the pseudo code below. In other words, the elements just belowthe main diagonal of the two-dimensional table C, those with index[i+1,i], are initialized by setting them to zero. The correspondingbit-string in the elements with index [i+1, i] in the PS table isinitialized to A, which denotes an empty string. Lines 4-7 handle andprocess singletons of type [i,i], stored in the main diagonal of thetables. Since there is an assumption that the surrounding elements ofthe ranges under consideration are both of type NM, in one embodiment,the present invention compares the size s(i) of the matching elementwith the cost of defining the matching element as non-matching, andletting the matching element be absorbed by the neighboring NM elements.In that case, two elements of the meta-data can be saved, which ischecked in line 4.

The main loop of the pseudo code starts then on line 9. Thetwo-dimensional tables C and PS are filled primarily by diagonals, eachcorresponding to a constant difference diff=j−i between the row andcolumn indices i and j, and within each diagonal, by increasing i. Line11 redefines j just for notational convenience.

In lines 12-13 of the pseudo code, the table entries are given defaultvalues, corresponding to the extreme case of all diff+1 elements in therange between and including i and j remaining matching as initiallygiven in the input. This corresponds to a bitstring of diff+1 zeroes‘000 . . . 0’ in the PS matrix. As to the cost of the default partition,diff+1 meta data blocks are stored, at the total price of (diff+1)FE.

After having initialized the table C, the loop starting in line 15 ofthe pseudo code tries to partition the range (i,j) into two sub-pieces.The idea is to consider two possibilities for the optimal partition ofthe range: either all the diff+1 elements should remain matching, as itis assumed in the default setting that initializes the C[i,j] value ofthe element in line 12, or there is or there is at least one elementwhose index will be denoted by k, with i≦k≦j, which in the optimalpartition should be turned into a NM-element. The optimal solution isthen obtained by solving the problem recursively on the remainingsub-ranges (i,k−1) and (k+1,j). The advantage of this definition is thatthe surrounding elements of the sub-ranges, i−1 and k for (i,k−1), and kand j+1 for (k+1,j), are again both of type NM, so the same table C canbe used. In other words, in one embodiment, the globally optimal subsetis determined by recursively applying the optimal calculation operationon sub-ranges (i,k−1) and (k+1,j) wherein the elements surrounding thesub-ranges, i−1 and k for (i,k−1), and k and j+1 for (k+1,j), are of atype of the non-matching segments.

FIG. 6 is a diagram illustrating an alternative partition of a sub-rangein which aspects of the present invention may be realized. FIG. 6illustrates the alternative partition of a sub-range (i,j) into twodisjoint parts, with L denoting the leftmost element of the right range(k+1,j), and R denoting the rightmost element of the left range (i,k−1).The i and j are indices of matching elements (illustrated in FIG. 6 aswhite/clear boxes) that are adjacent to non-matching elements(illustrated in FIG. 6 as dark/shaded boxes).

However, to combine the optimal solutions of the sub-ranges into anoptimal solution for the entire range, the present invention needs toknow/determine whether the elements adjacent to the separating elementindexed as k are of type M or NM. For if one or both of the elementsadjacent to the separating element indexed as k are NM, the adjacentelements can be merged with the separating element itself, so themeta-data decreases by one or two elements, reducing the price by FE or2FE. As depicted in FIG. 6, showing an alternative partition of asub-range, L denotes the leftmost element of the right range (k+1,j),and R denotes the rightmost element of the left range (i,k−1). Thesevalues are assigned in lines 16-23 of the pseudo code, includingtreatment of extreme cases. The general case is depicted in FIG. 6. Thepresent invention requires a function f(L,R), giving the number ofadditional meta-data elements needed as function of the type, 0 or 1,corresponding, respectively, to matching M or non-matching NM elements,of the bordering elements L and R. This function f(L, R) should givevalues according to the table appearing in FIG. 6 just below thealternative partition of the sub-ranges. A possible function is thusf(L,R)=1−L−R, which explains the definition of the variable newcost inline 24 of the pseudo code. The present invention then checks the sum ofthe costs of the optimal solutions of the sub-problems (i,k−1) and(k+1,j), plus the cost caused by the separating element k, and keeps thesmallest sum of these costs, over all the possible partition points k,in the table C at entry C[i,j]. Mathematically, this is represented bythe equation:

C[i,j]←min[(diff+1)FE,min_(i≦k≦j)(C[i,k−1]+C[k+1,j]+s(k)+(1−R−L)FE)]  (3),

where i and j are indicies of elements, L and R are the borderingelements as described above, F is the multiplicative factor and isvariable, E is the size of a pointer, and k is the index of theseparating element in the alternative partition of sub-ranges. In otherwords, in order to calculate the value of an element C[i,j] of thematrix, one only needs to refer to elements to its left in the same row,and to elements below it in the same column. This is schematicallyillustrated in the lower matrix of FIG. 5.

In other words, the problem is finding the optimal partition of (i,j).The 2 sub-problem are: finding the optimal partition of (i,k−1), andfinding the optimal partition of (k+1,j). The cost for (i,j) can beobtained by adding the costs for (i,k−1) plus the cost for (k+1,j) plusthe cost of combining everything, that is, the cost related to theseparating element k. However, at this point it is unknown what is theoptimal value of k. As such, in one embodiment, the present inventionmay try all the possible values of k, from i to j, and calculate thetotal cost. The minimal such cost is the optimal value that is soughtand the index k is recorded for which this minimum is obtained. Thisindex k is denoted below as OK (optimal k).

The index k between i and j for which the optimal partition has beenfound, if at all, i.e., the optimal partition (e.g., the globallyoptimal subset of the input partition) with the minimum cost, will bestored in a variable denoted OK. If the default value has been changed,the optimal solution, expressed as a bitstring of length diff+1, isobtained in line 30 of the pseudo code by concatenating the bitstringscorresponding to the optimal solutions of each of the subranges andbetween them the string ‘1’ corresponding to the element indexed k. Theoperator ∥ denotes concatenation. The formal pseudo code is given below.

1 C[n + 1,n] ← 0   PS[n + 1,n] ← Λ 2 for i ← 1 to n 3  C[i,i − 1] ←0      PS[i,i − 1] ← Λ 4  if s(i) − FE < FE then 5   C[i,i] ← s(i) −FE       PS[i,i] ←′1′ 6  else 7   C[i,i] ← FE          PS[i,i] ←′0′ 8end for i 9 for diff ← 1 to n − 1 10  for i ← 1 to n − diff 11   j ← i +diff 12   C[i,j] ← (diff + 1)FE 13   PS[i,j]←′000...0′ // (lengthdiff + 1) 14   OK ← 0 15   for k ← i to j 16    if k = j then 17     L ←1 18    else 19     L ← left(PS[k + 1,j]) 20    if k = i then 21     R ←1 22    else 23     R ← right(PS[i,k − 1]) 24    newcost ← C[i,k − 1] +C[k + 1,j] + s(k) + (1 − L − R)FE 25    if newcost < C[i,j] then 26    C[i,j] ← newcost 27     OK ← k 28   end for k 29   if OK > 0 then 30   PS[i,j] ← PS[i,OK − 1] ∥ ′1′ ∥ PS[OK + 1,j] 31  end for i 32 end fordiff

The complexity of evaluating the dynamic two-dimensional tables isdominated by the loops starting at line number 9. As illustrated abovein the pseudo code, for the various iterations performed by the presentinvention, there are three nested loops, and the loop on k goes from ito j−i=i+diff−1, so the iterations are executed a number of times equalto the constant difference diff, for each possible value of diff and i.The total number of iterations is therefore:

$\begin{matrix}{{{\sum\limits_{i = 1}^{n - 1}{i\left( {n - i} \right)}} = {\left\lbrack {{n\frac{n\left( {n - 1} \right)}{2}} - \frac{\left( {n - 1} \right){n\left( {{2n} - 1} \right)}}{6}} \right\rbrack = \frac{n^{3} - n}{6}}},} & (3)\end{matrix}$

where n is the input parameter of the number of consecutive blocks dealtwith in each call to the program for the optimal partition (e.g., theglobally optimal subset), and is the number of consecutive matchingitems between two non-matching ones.

Such a cubic number of iterations might be prohibitive, even though thecoefficient of n³ is at most 0.17. In terms of the bit-string notation:the result of applying the deduplication algorithm/calculation operationof a large input chunk is a sequence of matching or non-matching items,which is denoted by a bit-string of the form, e.g.,10010001011100000001000 . . . . The optimal partition (the globaloptimal subset) algorithm is then invoked for each of the 0-bit runs,which, on the given example, are of lengths 2, 3, 1, 0, 0, 7, etc. Thereis no need to call the procedure when n=0.

If certain values of n are too large, the present invention as describedabove may try to reduce the time complexity a priori by applying apreliminary filtering heuristic that will not impair the optimalsolution. For example, in one embodiment, the present invention mayconsider the maximal possible gain from declaring a matching item (e.g.,indicated by a “0”) to be non-matching (e.g., indicated by a “1”). Thishappens if the two adjacent blocks are non-matching themselves, and thenall 3 separate items could be merged into a single segment. The savingswould then be equivalent to 2FE bytes, which have to be counterbalancedby the loss of s(i) bytes that are not referenced anymore, so have to bestored explicitly. Thus, if s(i)>2FE, the i-th M-element will surely notbe transformed into an NM-element. It follows that s(i)>2FE is asufficient condition for keeping the value of the i-th bit in theoptimal partition as 0.

The heuristic will then scan all the input items and check thiscondition for each 0-item. If the condition holds, the element can bedeclared to remain of type 0, which partitions the rest of the elementsinto two parts. In other words, each element of the input partition isscanned for verifying the condition (e.g., that the value of thefunction s(i) is greater that 2FE bytes, s(i)>2FE, where F is themultiplicative factor and is a variable, and b is the size of a pointer,and s(i) is the second function for measuring, for both the matchingsegments and the non-matching segments, a size of data on a disk. Forexample, if the middle element of the n elements is thereby declared askeeping its 0-status, the present invention has split the n elementsinto two parts of size n/2 each, so the complexity is reduced from ⅙*n³to 2⅙(n/2)³= 1/24*n³. Now, returning to the example bit-string listed as10010001011100000001000 . . . , in one embodiment, if the boldfaced andunderlined elements are those fixed by the heuristic in their 0-status,the algorithm will be invoked with lengths 1, 1, 1, 1, 3, 2, etc.Theoretically, in this scenario the worst case did not change, evenafter applying this heuristic, but in practice, the largest values of nmight be much smaller. Indeed, one of the possible worst cases would bethat the condition s(i)>2FE does not hold for any index i, in which casethe heuristic would remain with the same value on n as before. Anotherbad case from the point of view of the time complexity would be thateven though there is an index i for which s(i)>2FE, this index is on theborder, e.g., i=1 or i=n, so the range between 1 and n would be splitinto two parts, but not both of size n/2 as in the example above, butrather one of size 0 and the other of size n−1; the result would be thatthere is hardly any reduction in the time complexity of the algorithm.

There remains a technical problem: the optimal partition evaluated inthe C[i,j] matrix is based on the assumption that the surroundingelements i−1 and j+1 were of type 1 (e.g., non-matching type elements),and if the above heuristic is applied, this assumption is notnecessarily true. Three approaches are possible to confront this problemby the present invention. First, in one embodiment, the illustratedembodiments described herein may use the value of C[i,j] and thecorresponding partition in PS [i,j] and adapt the value locally to thecases if one of the surrounding elements is 0. For example, if therightmost bit in PS [i,j] is 0, and bit j+1 is also 0, then noadaptation is needed; but if the rightmost bit in PS [i,j] is 1, and bitj+1 is 0, then the optimal values of C[i,j] took into account thatelements j and j+1 were merged, which is not true in our case, so thevalue of C[i,j] has to be increased by one meta-data element, that is byFE. A similar adaptation is needed for the left extremity, element i−1.

Such an adaptation is not necessary optimal, since it might be possiblethat, had it been known that both of the surrounding elements are not 1,an altogether different solution may be optimal. If the first approachconsisted of adapting a solution that is optimal for a different case,and thereby getting a possibly sub-optimal solution for the problem athand, the second approach might try to prove that this adaptation indeedyields a global optimum.

As a third approach, the definition of the first dynamic two-dimensionaltable represented by a C[i,j] matrix could be extended to be afour-dimensional table with C[i,j,L,R] being the global cost of theglobally optimal subset of elements i, i+1, . . . , j, under theassumption that the bordering elements, being i−1 and j+1 are of thetype L and R, where L,Rε{0,1}, where L represents the left border of thematching segments and R represents the right border of the matchingsegments.

While the time complexity is θ(n³), the C[i,j] table needs only n²space. But the strings stored in the PS [i,j] table are of length j−i+1,so that the space for PS [i,j] is also θ(n³). The following embodimentreduces the time complexity and stores only a constant amount of bytes,for each entry at the cost of not giving the optimal partitionexplicitly, but providing enough information for the optimal partitionto be built in linear time.

The key to this reduction of the space complexity is storing in PS [i,j](which may also now be referred to as S[i,j] to avoid confusions) notthe string itself, but the value OK at which the range of [i,j] has beensplit in an optimal way (line 27), or 0, if no such value OK exists.Since the string PS [i,j] served also to provide information on itsextremal elements (left and right in lines 19 and 23), these elementshave now to be saved in tables LT and RT on their own. The updatedpseudo code/algorithm is given below.

1 C[n + 1,n] ← 0 2 for i ← 1 to n 3  C[i,i − 1] ← 0 4  if s(i) − FE < FEthen 5   C[i,i] ← s(i) − FE LT[i,i] ← 1 RT[i,i] ← 1 6  else 7   C[i,i] ←FE    LT[i,i] ← 0 RT[i,i] ← 0 8 end for i 9 for diff ← 1 to n − 1 io for i ← 1 to n − diff 11   j ← i + diff 12   C[i,j] ← (diff + 1)FE 14  S[i,j] ← 0 15   for k ← i to j 16    if k = j then 17     L ← 1 18   else 19     L ← LT[k + 1,j] 20    if k = i then 21     R ← 1 22   else 23     R ← RT[i,k − 1] 24    newcost ← C[i,k − 1] + C[k + 1,j] +s(k) + (1 − L − R)FE 25    if newcost < C[i,j] then 26     C[i,j] ←newcost 27     S[i,j] ← k 28   end for k 29   LT[i,j] ← LT[i,j − 1] 30  RT[i,j] ← RT[i + 1,j] 31  end for i 32 end for diff

It should be noted how the elements of LT and RT are updated in eachiteration, just referring to shorter ranges of [i,j], that is, a rangewith smaller diff, which has therefore been treated earlier; LT is thencopied from a shorter range with the same left border, and RT is copiedfrom a shorter range with the same right border. While the timecomplexity remains θ(n³), the space complexity has been reduced, sinceall the saved tables, C, S, LT and RT are of size O(n²). The table S isonly defined for j≧i, the others for j≧i. To get the optimal partitionfor a given sequence of n elements, represented in the form of abit-string as those stored in the earlier version in the PS matrix, therecursive procedure build_vector with parameters (1, n), as illustratedin the pseudo code below, may be invoked. The procedure either returns astring of zeros, in case no 0-element should be turned into a 1-element,or it retrieves the index k of a 1-element from the table S and thencontinues recursively on the elements below k and on the elements abovek. The running time of build_vector (1,n) is O(n). Formally, theprocedure is defined by:

 build_vector(i,j) 1   if j > i then 2    k ← S[i,j] 3    if k = 0 then4     return ′000...0′ // (length j − i + 1) 5    else 6     returnbuild_vector (i,k − 1) ∥ ′1′ ∥ build_vector (k + 1,j)

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that may contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wired, optical fiber cable, RF, etc., or any suitable combination of theforegoing. Computer program code for carrying out operations for aspectsof the present invention may be written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Java, Smalltalk, C++ or the like and conventionalprocedural programming languages, such as the “C” programming languageor similar programming languages. The program code may execute entirelyon the user's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention have been described above withreference to flowchart illustrations and/or block diagrams of methods,apparatus (systems) and computer program products according toembodiments of the invention. It will be understood that each block ofthe flowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, may beimplemented by computer program instructions. These computer programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

These computer program instructions may also be stored in a computerreadable medium that may direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks. The computer program instructions may also beloaded onto a computer, other programmable data processing apparatus, orother devices to cause a series of operational steps to be performed onthe computer, other programmable apparatus or other devices to produce acomputer implemented process such that the instructions which execute onthe computer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the above figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, may be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

What is claimed is:
 1. A method for optimizing a partition of a datablock into matching and non-matching segments in data deduplicationusing a processor device in a computing environment, the methodcomprising: applying an optimal calculation operation in polynomial timeto the matching segments for selecting a globally optimal subset of aset of the matching segments according to overhead considerations forminimizing an overall size of a deduplicated file by determining a tradeoff between a time complexity and a space complexity.
 2. The method ofclaim 1, further including: applying the optimal calculation operationto the matching segments for determining which of the matching segmentsshould be preserved, and declaring each of the matching segments thatare determined not to be preserved as new segments added to thenon-matching segments.
 3. The method of claim 1, further includingapplying the optimal calculation operation on each sequence of thematching segments that are surrounded on both ends of the matchingsegments by two of the non-matching segments.
 4. The method of claim 1,further including performing each of: splitting a sequence of thematching segments into sub-parts for obtaining the globally optimalsubset, applying the optimal calculation operation on the sub-parts ofthe matching segments, and combining solutions of the optimalcalculation operation of the sub-parts into the optimal calculationoperation for an entire range of the sequence of the matching segments.5. The method of claim 1, further including: applying a plurality offunctions for determining a trade off between the time complexity andthe space complexity, wherein the plurality of functions include atleast a first function for determining a global cost of storing pointersin meta-data and a second function for measuring, for both the matchingsegments and the non-matching segments, a size of data on a disk, andusing a multiplicative factor for counting one byte of meta-data asequivalent to F-bytes of data that are written to the disk, wherein F isthe multiplicative factor and is variable.
 6. The method of claim 4,further including building the globally optimal subset by means of afirst two-dimensional table represented by a matrix C[i,j], and storinga representation of the globally optimal subset in a secondtwo-dimensional table represented by a matrix PS[i,j] that holds, atentry [i,j] of the matrix, the globally optimal subset for a pluralityof parameters in form of a bit-string of length j−i+1, wherein i and jare indices of bit positions corresponding to segments.
 7. The method ofclaim 6, wherein for 1≦i≦j≦n, C[i,j] is a global cost of the globallyoptimal partition of a sub-sequence of elements i, i+1, . . . , j−1, j,when surrounding elements i−1 and j+1 are of the non-matching segmentstype, where n is a number of elements and a number of rows and columnsin the matrix C.
 8. The method of claim 7, further including:initializing the first two-dimensional table by storing optimal valuesin a main diagonal of the first two-dimensional table at C[i,i], for1≦i≦n and just below the main diagonal of the first two-dimensionaltable at C[i,i−1], wherein default values are provided for initializingthe global cost, and ordering a plurality of iterations by an increasingdifference between j and i for applying the optimal calculationoperation to the matching segments for selecting the globally optimalsubset.
 9. The method of claim 8, further including, upon filling up thefirst two-dimensional table: retrieving the global cost of the globallyoptimal subset from the first two-dimensional table at location C[1,n],and the corresponding partition from the second two-dimensional table atlocation PS[1,n].
 10. The method of claim 6, further including:initializing the two-dimensional table by setting a global cost to zerofor ranges of a form [i+1,i] with a corresponding bit-string denoted asan empty string, and comparing, for the ranges of a form [i,i], a sizeof the matching segments with the global cost of defining the matchingsegments as being part of the non-matching segments.
 11. The method ofclaim 6, further including: filling the first two-dimensional table bydiagonals, each corresponding to a constant difference represented bythe equation diff=j−i, and within each of the diagonals, filling byincreasing i, wherein i is the index of a row, j is the index of acolumn and diff is a constant difference between j and i, assigningdefault values in the first two-dimensional table, corresponding to alldiff+1 elements in between i and j being defined as matching segments,wherein the default values correspond to a bitstring of diff+1 zeros inthe PS[i,j] matrix, wherein the global cost of a default partition isstored as diff+1 metadata blocks at a total global cost of (diff+1)FE,where F is the multiplicative factor and is variable and E is a size ofone pointer, determining the globally optimal subset by one of acceptingthe default partition of all of the diff+1 elements for a given rangeremaining as the matching segments and finding at least one element withindex k, with i k j which is turned into a non-matching element, andthen recursively applying the optimal calculation operation onsub-ranges (i,k−1) and (k+1,j), wherein elements surrounding thesub-ranges i−1 and k for (i,k−1), and k and j+1 for (k+1,j), are of atype of the non-matching segments, checking a sum of the global cost foreach possible partition of the range of (i,j) into two sub-ranges(i,k−1) and (k+1,j), and retaining the smallest sum over all possible kpartition points k in the first two-dimensional table.
 12. The method ofclaim 6, further including, performing one of: executing a firstcalculation operation for reducing the time complexity, and executing asecond calculation operation for reducing the space complexity.
 13. Themethod of claim 12, further including, for reducing the time complexity,performing one of: scanning each element indexed i of an input partitionfor verifying a sufficient condition for ensuring that a value in theglobally optimal subset for each element indexed i is equal to zero,wherein the condition is determined by the equation s(i)>2FE, where F isthe multiplicative factor and is a variable, and E a size of onepointer, and s(i) is a second function for measuring, for both thematching segments and the non-matching segments, a size of data on adisk, wherein if the condition holds, the element indexed i is declaredto remain of type 0, which partitions the rest of the elements into twoparts, and configuring the first two-dimensional table to become afour-dimensional table with C[i,j,L,R] being the global cost of theglobally optimal subset of elements i, i+1, . . . , j, with borderingelements i−1 and j+1 being of type L and R, where L,Rε{0,1}.
 14. Themethod of claim 6, further including, for reducing the space complexity,storing in the PS[i,j] matrix at an entry indexed (i,j) one of adetermined value at which a range of (i,j) of the matching segments hasbeen split into sub-parts for obtaining the optimal subset for therange, and zero if the value does not exist.
 15. A system for optimizinga partition of a data block into matching and non-matching segments indata deduplication using a processor device in a computing environment,the system comprising: a processor device operable in the computingstorage environment, wherein the processor device for optimizing aninput partition of the data block having both the matching segments andthe non-matching segments: applies an optimal calculation operation inpolynomial time to the matching segments for selecting a globallyoptimal subset of a set of the matching segments according to overheadconsiderations for minimizing an overall size of a deduplicated file bydetermining a trade off between a time complexity and a spacecomplexity.
 16. The system of claim 15, wherein the processor device:applies the optimal calculation operation to the matching segments fordetermining which of the matching segments should be preserved, anddeclares each of the matching segments that are determined not to bepreserved as new segments added to the non-matching segments.
 17. Thesystem of claim 15, wherein the processor device applies the optimalcalculation operation on each sequence of the matching segments that aresurrounded on both ends of the matching segments by two of thenon-matching segments.
 18. The system of claim 15, wherein the processordevice performs each of: splitting a sequence of the matching segmentsinto sub-parts for obtaining the globally optimal subset, applying theoptimal calculation operation on the sub-parts of the matching segments,and combining solutions of the optimal calculation operation of thesub-parts into the optimal calculation operation for an entire range ofthe sequence of the matching segments.
 19. The system of claim 15,wherein the processor device: applies a plurality of functions fordetermining a trade off between the time complexity and the spacecomplexity, wherein the plurality of functions include at least a firstfunction for determining a global cost of storing pointers in meta-dataand a second function for measuring, for both the matching segments andthe non-matching segments, a size of data on a disk, uses amultiplicative factor for counting one byte of meta-data as equivalentto F-bytes of data that are written to the disk, wherein F is themultiplicative factor and is variable.
 20. The system of claim 18,wherein the processor device builds the globally optimal subset by meansof a first two-dimensional table represented by a matrix C[i,j], andstoring a representation of the globally optimal subset in a secondtwo-dimensional table represented by a matrix PS[i,j] that holds, atentry [i,j] of the matrix, the globally optimal subset for a pluralityof parameters in form of a bit-string of length j−i+1, wherein i and jare indices of bit positions corresponding to segments.
 21. The systemof claim 20, wherein for 1≦i≦j≦n, C[i,j] is a global cost of theglobally optimal partition of a sub-sequence of elements i, i+1, . . . ,j−1, j, when surrounding elements i−1 and j+1 are of the non-matchingsegments type, where n is a number of elements and a number of rows andcolumns in the matrix C.
 22. The system of claim 21, wherein theprocessor device: initializes the first two-dimensional table by storingoptimal values in a main diagonal of the first two-dimensional table atC[i,i], for 1≦i≦n and just below the main diagonal of the firsttwo-dimensional table at C[i, i−1], wherein default values are providedfor initializing the global cost, and orders a plurality of iterationsby an increasing difference between j and i for applying the optimalcalculation operation to the matching segments for selecting theglobally optimal subset.
 23. The system of claim 22, wherein theprocessor device, upon filling up the first two-dimensional table:retrieves the global cost of the globally optimal subset from the firsttwo-dimensional table at location C[1,n], and the correspondingpartition from the second two-dimensional table at location PS[1,n]. 24.The system of claim 20, wherein the processor device: initializes thetwo-dimensional table by setting a global cost to zero for ranges of aform [i+1,i] with a corresponding bit-string denoted as an empty string,and compares, for the ranges of a form [i,i], a size of the matchingsegments with the global cost of defining the matching segments as beingpart of the non-matching segments.
 25. The system of claim 20, whereinthe processor device: fills the first two-dimensional table bydiagonals, each corresponding to a constant difference represented bythe equation diff=j−i, and within each of the diagonals, filling byincreasing i, wherein i is the index of a row, j is the index of acolumn and diff is a constant difference between j and i, assignsdefault values in the first two-dimensional table, corresponding to alldiff+1 elements in between i and j being defined as matching segments,wherein the default values correspond to a bitstring of diff+1 zeros inthe PS[i,j] matrix, wherein the global cost of a default partition isstored as diff+1 metadata blocks at a total global cost of(diff+1)FE,where F is the multiplicative factor and is variable and E is a size ofone pointer, determines the globally optimal subset by one of acceptingthe default partition of all of the diff+1 elements for a given rangeremaining as the matching segments and finding at least one element withindex k, with i≦k≦j which is turned into a non-matching element, andthen recursively applying the optimal calculation operation onsub-ranges (i,k−1) and (k+1,j), wherein elements surrounding thesub-ranges i−1 and k for (i,k−1), and k and j+1 for (k+1,j), are of atype of the non-matching segments, checks a sum of the global cost foreach possible partition of the range of (i,j) into two sub-ranges(i,k−1) and (k+1,j), and retains the smallest sum over all possible kpartition points k in the first two-dimensional table.
 26. The system ofclaim 20, wherein the processor device performs one of: executing afirst calculation operation for reducing the time complexity, andexecuting a second calculation operation for reducing the spacecomplexity.
 27. The system of claim 26, wherein the processor device,for reducing the time complexity, performs one of: scans each elementindexed i of the input partition for verifying a sufficient conditionfor ensuring that a value in the globally optimal subset for eachelement indexed i is equal to zero, wherein the condition is determinedby the equation s(i)>2FE, where F is the multiplicative factor and is avariable, and E a size of one pointer, and s(i) is a second function formeasuring, for both the matching segments and the non-matching segments,a size of data on a disk, wherein if the condition holds, the elementindexed i is declared to remain of type 0, which partitions the rest ofthe elements into two parts, and configures the first two-dimensionaltable to become a four-dimensional table with C[i,j,L,R] being theglobal cost of the globally optimal subset of elements i, i+1, . . . ,j, with bordering elements i−1 and j+1 being of type L and R, whereL,Rε{0,1}.
 28. The system of claim 20, wherein the processor device, forreducing the space complexity, stores in the PS[i,j] matrix at an entryindexed (i,j) one of a determined value at which a range of (i,j) of thematching segments has been split into sub-parts for obtaining theoptimal subset for the range, and zero if the value does not exist. 29.A computer program product for optimizing a partition of a data blockinto matching and non-matching segments in data deduplication using aprocessor device in a computing environment, the computer programproduct comprising a computer-readable storage medium havingcomputer-readable program code portions stored therein, thecomputer-readable program code portions comprising: a first executableportion that applies an optimal calculation operation in polynomial timeto the matching segments for selecting a globally optimal subset of aset of the matching segments according to overhead considerations forminimizing an overall size of a deduplicated file by determining a tradeoff between a time complexity and a space complexity.
 30. The computerprogram product of claim 29, further including a second executableportion that: applies the optimal calculation operation to the matchingsegments for determining which of the matching segments should bepreserved, and declaring each of the matching segments that aredetermined not to be preserved as new segments added to the non-matchingsegments.
 31. The computer program product of claim 29, furtherincluding a third executable portion that applies the optimalcalculation operation on each sequence of the matching segments that aresurrounded on both ends of the matching segments by two of thenon-matching segments.
 32. The computer program product of claim 29,further including a third executable portion that performs each of:splitting a sequence of the matching segments into sub-parts forobtaining the globally optimal subset, and applying the optimalcalculation operation on the sub-parts of the matching segments, andcombining solutions of the optimal calculation operation of thesub-parts into the optimal calculation operation for an entire range ofthe sequence of the matching segments.
 33. The computer program productof claim 29, further including a third executable portion that: appliesa plurality of functions for determining a trade off between the timecomplexity and the space complexity, wherein the plurality of functionsinclude at least a first function for determining a global cost ofstoring pointers in meta-data and a second function for measuring, forboth the matching segments and the non-matching segments, a size of dataon a disk, and uses a multiplicative factor for counting one byte ofmeta-data as equivalent to F-bytes of data that are written to the disk,wherein F is the multiplicative factor and is variable.
 34. The computerprogram product of claim 32, further including a fourth executableportion that builds the globally optimal subset by means of a firsttwo-dimensional table represented by a matrix C[i,j], and storing arepresentation of the globally optimal subset in a secondtwo-dimensional table represented by a matrix PS[i,j] that holds, atentry [i,j] of the matrix, the globally optimal subset for a pluralityof parameters in form of a bit-string of length j−i+1, wherein i and jare indices of bit positions corresponding to segments.
 35. The computerprogram product of claim 34, wherein for 1≦i≦j≦n, C[i,j] is a globalcost of the globally optimal partition of a sub-sequence of elements i,i+1, . . . , j−1, j, when surrounding elements i−1 and j+1 are of thenon-matching segments type, where n is a number of elements and a numberof rows and columns in the matrix C.
 36. The computer program product ofclaim 35, further including a fifth executable portion that: initializesthe first two-dimensional table by storing optimal values in a maindiagonal of the first two-dimensional table at C[i,i], for 1≦i≦n andjust below the main diagonal of the first two-dimensional table atC[i,i−1], wherein default values are provided for initializing theglobal cost, and orders a plurality of iterations by an increasingdifference between j and i for applying the optimal calculationoperation to the matching segments for selecting the globally optimalsubset.
 37. The computer program product of claim 36, further includinga sixth executable portion that, upon filling up the firsttwo-dimensional table: retrieves the global cost of the globally optimalsubset from the first two-dimensional table at location C[1,n], and thecorresponding partition from the second two-dimensional table atlocation PS[1,n].
 38. The computer program product of claim 34, furtherincluding a fifth executable portion that: initializes thetwo-dimensional table by setting a global cost to zero for ranges of aform [i+1,i] with a corresponding bit-string denoted as an empty string,and compares, for the ranges of a form [i,i], a size of the matchingsegments with the global cost of defining the matching segments as beingpart of the non-matching segments.
 39. The computer program product ofclaim 34, further including a fifth executable portion that: fills thefirst two-dimensional table by diagonals, each corresponding to aconstant difference represented by the equation diff=j−i, and withineach of the diagonals, filling by increasing i, wherein i is the indexof a row, j is the index of a column and diff is a constant differencebetween j and i, assigns default values in the first two-dimensionaltable, corresponding to all diff+1 elements in between i and j beingdefined as matching segments, wherein the default values correspond to abitstring of diff+1 zeros in the PS[i,j] matrix, wherein the global costof a default partition is stored as diff+1 metadata blocks at a totalglobal cost of (diff+1)FE, where F is the multiplicative factor and isvariable and E is a size of one pointer, determines the globally optimalsubset by one of accepting the default partition of all of the diff+1elements for a given range remaining as the matching segments andfinding at least one element with index k, with i≦k≦j which is turnedinto a non-matching element, and then recursively applying the optimalcalculation operation on sub-ranges (i,k−1) and (k+1,j), whereinelements surrounding the sub-ranges i−1 and k for (i,k−1), and k and j+1for (k+1,j), are of a type of the non-matching segments, checks a sum ofthe global cost for each possible partition of the range of (i,j) intotwo sub-ranges (i,k−1) and (k+1,j), and retains the smallest sum overall possible k partition points k in the first two-dimensional table.40. The computer program product of claim 34, further including a fifthexecutable portion that performs one of: executing a first calculationoperation for reducing the time complexity, and executing a secondcalculation operation for reducing the space complexity.
 41. Thecomputer program product of claim 40, further including a sixthexecutable portion that, for reducing the time complexity, performs oneof: scanning each element indexed i of an input partition for verifyinga sufficient condition for ensuring that a value in the globally optimalsubset for each element indexed i is equal to zero, wherein thecondition is determined by the equation s(i)>2FE, where F is themultiplicative factor and is a variable, and E a size of one pointer,and s(i) is a second function for measuring, for both the matchingsegments and the non-matching segments, a size of data on a disk,wherein if the condition holds, the element indexed i is declared toremain of type 0, which partitions the rest of the elements into twoparts, and configuring the first two-dimensional table to become afour-dimensional table with C[i,j,L,R] being the global cost of theglobally optimal subset of elements i, i+1, . . . , j, with borderingelements i−1 and j+1 being of type L and R, where L,Rε{0,1}.
 42. Thecomputer program product of claim 34, further including a fifthexecutable portion that performs, for reducing the space complexity,stores in the PS[i,j] matrix at an entry indexed (i,j) one of adetermined value at which a range of (i,j) of the matching segments hasbeen split into sub-parts for obtaining the optimal subset for therange, and zero if the value does not exist.